Related Experiment Video

Updated: Jul 5, 2025

Fetal Echocardiography and Pulsed-wave Doppler Ultrasound in a Rabbit Model of Intrauterine Growth Restriction
14:19

Fetal Echocardiography and Pulsed-wave Doppler Ultrasound in a Rabbit Model of Intrauterine Growth Restriction

Published on: June 29, 2013

28.2K

Prognosticating Fetal Growth Restriction and Small for Gestational Age by Medical History

Herdiantri Sufriyana1,2, Fariska Zata Amani3, Aufar Zimamuz Zaman Al Hajiri4

  • 1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan.

Studies in Health Technology and Informatics
|January 25, 2024
PubMed

Insights

A new deep learning model effectively screens for fetal growth restriction (FGR) and small for gestational age (SGA) using only medical history. This approach shows moderate accuracy, paving the way for improved prenatal care.

Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Obstetrics and Gynecology

Background:

  • Fetal growth restriction (FGR) and small for gestational age (SGA) are critical indicators of fetal well-being.
  • Accurate screening is essential for timely intervention and improved neonatal outcomes.
  • Existing screening methods often require complex clinical assessments.

Purpose of the Study:

  • To develop and validate a prognostic prediction model for FGR/SGA using only historical medical data.
  • To leverage machine learning, including deep learning, for enhanced predictive accuracy.
  • To identify key medical history predictors for FGR/SGA.

Main Methods:

  • Retrospective analysis of a nationwide health insurance database (n=1,697,452) for females aged 12-55.
  • Application of machine learning algorithms, including a deep-insight visible neural network (DI-VNN).
  • Utilized 54 distinct medical history predictors for model training and validation.

Main Results:

  • The DI-VNN model achieved an area under the curve (AUROC) of 0.742 (95% CI 0.734-0.750).
  • The model demonstrated a sensitivity of 49.09% (95% CI 47.60%-50.58%) at 95% specificity.
  • Medical history alone, processed by DI-VNN, provided moderate accuracy in screening for FGR/SGA.

Conclusions:

  • A deep learning model utilizing medical history shows potential for screening FGR/SGA.
  • The DI-VNN model offers a novel, data-driven approach to identifying at-risk pregnancies.
  • Further research will compare this model against existing methods and assess its clinical impact on patient outcomes.

Keywords:
Fetal growth restrictiondeep learningelectronic health recordsmachine learningrisk predictionsmall for gestational age

More Related Videos

High Frequency Ultrasound for the Analysis of Fetal and Placental Development In Vivo06:43

High Frequency Ultrasound for the Analysis of Fetal and Placental Development In Vivo

Published on: November 8, 2018

10.3K
Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System07:34

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System

Published on: May 5, 2018

11.6K

Related Concept Videos

Teratogenicity01:07

Teratogenicity

The ability of a drug to produce structural deformations and functional abnormalities in the developing embryo or the fetus is called teratogenicity, and the drug producing this effect is known as a teratogen. Teratogenic effects include stillbirth, miscarriage, intrauterine growth restriction, and neurocognitive delay. A teratogen may affect the embryo at different stages of development, which is important in determining the type and extent of the damage. During blastocyst formation, the early...
2.5K
Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
2.4K
Nature and Nurture01:10

Nature and Nurture

Many human characteristics, like height, are shaped by both nature—in other words, by our genes—and by nurture, or our environment. For example, chronic stress during childhood inhibits the production of growth hormones and consequently reduces bone growth and height. Scientists estimate that 70-90% of variation in height is due to genetic differences among individuals, and 10-30% of variation in height is due to differences in the environments that individuals experience,...
20.5K